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Thursday Note to Subscribers: $AMD “Our Top Idea into Friday, Pure High Confluence look for move to $250 -> $253” AMD 4-10 240C: +900% ($9 ITM) AMD 4-17 270C: +355% 🦠

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A+ Insight 🔪

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30,869 просмотров • 4 месяцев назад •via X (Twitter)

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Billionaire Bill Ackman’s best advice to investors in 2026… Sounds like we should just buy: 1) $VOO 2) $QQQM 3) $NVDA 4) $AMD 5) $MSFT 6) $SCHG 7) $MAGS 8) $SMH 9) $DRAM 10) $GOOG
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Billionaire Bill Ackman’s best advice to investors in 2026… Sounds like we should just buy: 1) $VOO 2) $QQQM 3) $NVDA 4) $AMD 5) $MSFT 6) $SCHG 7) $MAGS 8) $SMH 9) $DRAM 10) $GOOG

Marcos Milla

97,884 просмотров • 4 месяцев назад

Some time ago, I had the idea to port NVIDIA Physical AI stack to AMD. The motivation was to improve hardware diversity and enable world models and VLAs to run beyond a single ecosystem. We started with NVIDIA Cosmos Predict 2.5-2B. Porting wasn’t trivial: these models are deeply optimized for NVIDIA’s stack. We used this as an opportunity to apply our ROCm kernels. The results were surprising: Both encode and diffusion run faster on AMD Instinct MI300X vs. NVIDIA H200 (FA3) and we still saw significant headroom for further optimization. Quality is unchanged across modalities (validated with WorldJen) To be clear, this is no luck. We have deep experience with diffusion models and AMD GPUs. But this just gives us a good opportunity to get closer to a true hardware-to-hardware comparison, as we work with less software abstractions than usual. Just to give an example, on AMD, memory instructions are async with a hardware queue of ordered pending instructions, enabling concurrent load/store with compute without warp specialization. Bottom line: there are real architectural advantages on AMD, if you take the time to work with the hardware. Note, we did tradeoff ~20% higher memory usage, That being said, AMD has more to give to begin with :) in the coming weeks: AMD versions of Cosmos Transfer and GR00T, an even faster version of Cosmos Predict, and open-sourcing an attention kernel faster than AITER v3 (which is closed-source for some reason? cc: Anush Elangovan )
0:44

Some time ago, I had the idea to port NVIDIA Physical AI stack to AMD. The motivation was to improve hardware diversity and enable world models and VLAs to run beyond a single ecosystem. We started with NVIDIA Cosmos Predict 2.5-2B. Porting wasn’t trivial: these models are deeply optimized for NVIDIA’s stack. We used this as an opportunity to apply our ROCm kernels. The results were surprising: Both encode and diffusion run faster on AMD Instinct MI300X vs. NVIDIA H200 (FA3) and we still saw significant headroom for further optimization. Quality is unchanged across modalities (validated with WorldJen) To be clear, this is no luck. We have deep experience with diffusion models and AMD GPUs. But this just gives us a good opportunity to get closer to a true hardware-to-hardware comparison, as we work with less software abstractions than usual. Just to give an example, on AMD, memory instructions are async with a hardware queue of ordered pending instructions, enabling concurrent load/store with compute without warp specialization. Bottom line: there are real architectural advantages on AMD, if you take the time to work with the hardware. Note, we did tradeoff ~20% higher memory usage, That being said, AMD has more to give to begin with :) in the coming weeks: AMD versions of Cosmos Transfer and GR00T, an even faster version of Cosmos Predict, and open-sourcing an attention kernel faster than AITER v3 (which is closed-source for some reason? cc: Anush Elangovan )

Omer Shlomovits

36,648 просмотров • 4 месяцев назад

Tom Lee predicts that the stock market could go into a bear market between August & October… Both: $VOO & $QQQM could drop a lot… On the bright side, July should be a great month for stocks… However, if huge red days do occur… It’s a buy the dip opportunity for: 1) $QQQM 2) $VOO 3) $DRAM 4) $VGT 5) $XLK 6) $SPMO 7) $SCHG 8) $SMH 9) $VTI 10) $SOXX 11) NVIDIA $NVDA 12) Micron $MU 13) Sandisk $SNDK 14) Meta $META 15) Google $GOOG 16) AMD $AMD 17) SK Hynix $SKHY 18) Lumentum $LITE 19) TSMC $TSM 20) Microsoft $MSFT
1:25

Tom Lee predicts that the stock market could go into a bear market between August & October… Both: $VOO & $QQQM could drop a lot… On the bright side, July should be a great month for stocks… However, if huge red days do occur… It’s a buy the dip opportunity for: 1) $QQQM 2) $VOO 3) $DRAM 4) $VGT 5) $XLK 6) $SPMO 7) $SCHG 8) $SMH 9) $VTI 10) $SOXX 11) NVIDIA $NVDA 12) Micron $MU 13) Sandisk $SNDK 14) Meta $META 15) Google $GOOG 16) AMD $AMD 17) SK Hynix $SKHY 18) Lumentum $LITE 19) TSMC $TSM 20) Microsoft $MSFT

Marcos Milla

129,654 просмотров • 1 месяц назад

The official release of Once Human is set for July 9th, 2024 (Pacific Time)! To celebrate, we're hosting a giveaway! ┉┉▾Giveaway▾┉┉ Follow Once Human and repost this post to join. • Event period: Before 23:59 June 17th PT. • Basic Rewards: - Steam Deck 1 TB OLED×1 - Steam Gift Card: $100×5 - Steam Gift Card: $50×10 And that's not all! If any of the following milestones are hit, the prizes get even better! • Giveaway Upgrades: - Reach 3,000 engagements (likes, comments, reposts): + Steam Deck×1 - Reach 50,000 followers: + Steam Decks×1 - During Steam Next Fest, if Once Human reaches Steam's "Most Played" Top 10: + AMD×Once Human limited co-branded host [AMD Radeon™ RX 7900 Series GPU, AMD Ryzen™ 9 7800X3D CPU] ×1 Don't forget to wishlist now and pre-download our Steam Next Fest demo starting June 7th. Get ready to start playing at 10:00 AM on June 10th PT! Wishlist on Steam: #OnceHuman
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The official release of Once Human is set for July 9th, 2024 (Pacific Time)! To celebrate, we're hosting a giveaway! ┉┉▾Giveaway▾┉┉ Follow Once Human and repost this post to join. • Event period: Before 23:59 June 17th PT. • Basic Rewards: - Steam Deck 1 TB OLED×1 - Steam Gift Card: $100×5 - Steam Gift Card: $50×10 And that's not all! If any of the following milestones are hit, the prizes get even better! • Giveaway Upgrades: - Reach 3,000 engagements (likes, comments, reposts): + Steam Deck×1 - Reach 50,000 followers: + Steam Decks×1 - During Steam Next Fest, if Once Human reaches Steam's "Most Played" Top 10: + AMD×Once Human limited co-branded host [AMD Radeon™ RX 7900 Series GPU, AMD Ryzen™ 9 7800X3D CPU] ×1 Don't forget to wishlist now and pre-download our Steam Next Fest demo starting June 7th. Get ready to start playing at 10:00 AM on June 10th PT! Wishlist on Steam: #OnceHuman

Once Human

1,833,318 просмотров • 2 лет назад

$AMD $NVDA & the AMD Bear SemiAnalysis 🧵 Here are some facts: $META allocated 42% AI GPUs to $AMD OpenAI allocated 6GW(38%) to $AMD 1. Model-Specific Bias: Llama 3.3 70B graph favored NVIDIA due to TRT-LLM optimizations, highlighting throughput and latency where Blackwell excels. In contrast, the GPT-OSS 120B chart shifts focus to cost and interactivity, where MI355X shines. This selective model choice clearly suggests SemiAnalysis tailors benchmarks to reinforce narratives—NVIDIA’s dominance in speed (Llama 3.3) and AMD’s niche in cost (GPT-OSS). GPT-OSS 120B, with its sparse attention mechanisms (similar to DeepSeek-V3.2-Exp), shows AMD’s CDNA 4 architecture, while Llama 3.3’s dense attention favors NVIDIA’s Tensor Cores. SemiAnalysis’ decision to emphasize Llama 3.3 initially could reflect its AMD bear stance. 2. The way Data is presented The Llama 3.3 graph focused on raw performance metrics (throughput vs. latency), downplaying cost, where AMD holds an edge. This new chart, buried in follow-up posts, reveals AMD’s strength but receives less prominence, suggesting a curated narrative. Labeling variability (e.g., B200 with/without TRT) and the lack of uniform scaling across graphs indicate potential cherry-picking of configurations to favor NVIDIA’s optimized setups. 3. Historical Context: SemiAnalysis’ past critiques of AMD’s R&D and ROCm (web results from May 2025) align with a bearish outlook. Their own hype/brand around NVIDIA’s 15x ROI contrasts with muted coverage of AMD’s cost advantages, reinforcing bias. Despite AMD’s participation in InferenceMAX, the benchmark’s framing (e.g., prioritizing Blackwell’s ROI) reflect SemiAnalysis’ market predictions rather than balanced analysis. Lastly, AMD’s Instinct MI355X proves superior in inference and cost per million tokens for the GPT-OSS 120B model, offering a 25% cost advantage over NVIDIA’s H200 at moderate-to-high interactivity levels. This efficiency, driven by AMD’s memory bandwidth and FP4 support, makes it a better choice for cost-sensitive, multi-user deployments over a three-year horizon. However, SemiAnalysis’ sole focus(presentation graph) on Llama 3.3—where NVIDIA excels demonstrates a pattern of cherry-picking models and data to favor NVIDIA , consistent with its historical AMD bearish stance. This selective presentation risks misleading stakeholders by overshadowing AMD economic strengths. My personal take: I would trust Dr. Lisa Su, and Greg Brockman Sam Altman take on AMD and how they viewed and allocated 6GW for AMD over SemiAnalysis . At the end of the day, Large customers pay when it works. $Meta allocated 42% AI GPUs to $AMD for a reason. And the "secret weapon" will improve energy consumption by 20-50%, meaning at 6GW, OpenAI would be able to deploy 25-50% more MI450 at a much better cost advantage, higher memory bandwidth, and the queen of Inference! Oh and ROCm 8 is expected to be on par with CUDA in 2026.
2:46

$AMD $NVDA & the AMD Bear SemiAnalysis 🧵 Here are some facts: $META allocated 42% AI GPUs to $AMD OpenAI allocated 6GW(38%) to $AMD 1. Model-Specific Bias: Llama 3.3 70B graph favored NVIDIA due to TRT-LLM optimizations, highlighting throughput and latency where Blackwell excels. In contrast, the GPT-OSS 120B chart shifts focus to cost and interactivity, where MI355X shines. This selective model choice clearly suggests SemiAnalysis tailors benchmarks to reinforce narratives—NVIDIA’s dominance in speed (Llama 3.3) and AMD’s niche in cost (GPT-OSS). GPT-OSS 120B, with its sparse attention mechanisms (similar to DeepSeek-V3.2-Exp), shows AMD’s CDNA 4 architecture, while Llama 3.3’s dense attention favors NVIDIA’s Tensor Cores. SemiAnalysis’ decision to emphasize Llama 3.3 initially could reflect its AMD bear stance. 2. The way Data is presented The Llama 3.3 graph focused on raw performance metrics (throughput vs. latency), downplaying cost, where AMD holds an edge. This new chart, buried in follow-up posts, reveals AMD’s strength but receives less prominence, suggesting a curated narrative. Labeling variability (e.g., B200 with/without TRT) and the lack of uniform scaling across graphs indicate potential cherry-picking of configurations to favor NVIDIA’s optimized setups. 3. Historical Context: SemiAnalysis’ past critiques of AMD’s R&D and ROCm (web results from May 2025) align with a bearish outlook. Their own hype/brand around NVIDIA’s 15x ROI contrasts with muted coverage of AMD’s cost advantages, reinforcing bias. Despite AMD’s participation in InferenceMAX, the benchmark’s framing (e.g., prioritizing Blackwell’s ROI) reflect SemiAnalysis’ market predictions rather than balanced analysis. Lastly, AMD’s Instinct MI355X proves superior in inference and cost per million tokens for the GPT-OSS 120B model, offering a 25% cost advantage over NVIDIA’s H200 at moderate-to-high interactivity levels. This efficiency, driven by AMD’s memory bandwidth and FP4 support, makes it a better choice for cost-sensitive, multi-user deployments over a three-year horizon. However, SemiAnalysis’ sole focus(presentation graph) on Llama 3.3—where NVIDIA excels demonstrates a pattern of cherry-picking models and data to favor NVIDIA , consistent with its historical AMD bearish stance. This selective presentation risks misleading stakeholders by overshadowing AMD economic strengths. My personal take: I would trust Dr. Lisa Su, and Greg Brockman Sam Altman take on AMD and how they viewed and allocated 6GW for AMD over SemiAnalysis . At the end of the day, Large customers pay when it works. $Meta allocated 42% AI GPUs to $AMD for a reason. And the "secret weapon" will improve energy consumption by 20-50%, meaning at 6GW, OpenAI would be able to deploy 25-50% more MI450 at a much better cost advantage, higher memory bandwidth, and the queen of Inference! Oh and ROCm 8 is expected to be on par with CUDA in 2026.

Mike

104,194 просмотров • 10 месяцев назад

I told you to buy all Mag 7's in 2025. We're up over 72% since then. Now, I am telling you to stay patient for the red days which will start next week. Buy the dip when it happens. Here's exactly when and where to buy my top 10: 1. Amazon $AMZN 2. Apple $AAPL 3. Advanced Micro Devices $AMD 4. Nvidia $NVDA 5. Meta Platforms $META 6. Microsoft $MSFT 7. Google $GOOGL 8. Tesla $TSLA 9. Netflix $NFLX 10. ExxonMobil $XOM All my buy and sell signals in Discord @
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I told you to buy all Mag 7's in 2025. We're up over 72% since then. Now, I am telling you to stay patient for the red days which will start next week. Buy the dip when it happens. Here's exactly when and where to buy my top 10: 1. Amazon $AMZN 2. Apple $AAPL 3. Advanced Micro Devices $AMD 4. Nvidia $NVDA 5. Meta Platforms $META 6. Microsoft $MSFT 7. Google $GOOGL 8. Tesla $TSLA 9. Netflix $NFLX 10. ExxonMobil $XOM All my buy and sell signals in Discord @

StockWhale

219,516 просмотров • 4 месяцев назад

Building AI infrastructure at yotta-scale requires more than raw performance; it demands an open, modular rack design that can evolve across product generations, combining leadership compute engines with high-speed networking to connect thousands of accelerators into a single, unified system. Take a look at the Helios. Our rack-scale AI platform powered by MI455X GPUs and EPYC “Venice” CPUs and designed for trillion-parameter training.
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Building AI infrastructure at yotta-scale requires more than raw performance; it demands an open, modular rack design that can evolve across product generations, combining leadership compute engines with high-speed networking to connect thousands of accelerators into a single, unified system. Take a look at the Helios. Our rack-scale AI platform powered by MI455X GPUs and EPYC “Venice” CPUs and designed for trillion-parameter training.

AMD

20,832 просмотров • 7 месяцев назад

AMD CEO LISA SU JUST DOUBLED HER OWN CPU FORECAST IN 6 MONTHS In November, she told investors the CPU market would grow 18-20% per year through 2030. This morning she revised that number: "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." The driver: agents. Six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center. The new ratio is closer to 1 to 1. Su on CNBC: "Each of us could have 2 or 3 or even 5 or 10 agents doing work on our behalf. That means you need a lot more CPU compute."
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AMD CEO LISA SU JUST DOUBLED HER OWN CPU FORECAST IN 6 MONTHS In November, she told investors the CPU market would grow 18-20% per year through 2030. This morning she revised that number: "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." The driver: agents. Six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center. The new ratio is closer to 1 to 1. Su on CNBC: "Each of us could have 2 or 3 or even 5 or 10 agents doing work on our behalf. That means you need a lot more CPU compute."

WOLF

88,438 просмотров • 3 месяцев назад

Introducing HOME STRETCH: PVRIS. In 2014, PVRIS released their debut album 'White Noise' to critical acclaim catapulting the New England-born alternative act into the mainstream music scene. But for PVRIS, the success didn't happen overnight. Join us for our third installment of HOME STRETCH, as we head back to Lowell, Massachusetts to learn more about the band's come up. Episode 1 airs tomorrow at 9:30am PT / 12:30pm ET: Episode 2 out Thursday, 4/10 at 9:30am PT / 12:30pm ET Episode 3 out Thursday, 4/17 at 9:30am PT / 12:30pm PT #PVRIS
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Introducing HOME STRETCH: PVRIS. In 2014, PVRIS released their debut album 'White Noise' to critical acclaim catapulting the New England-born alternative act into the mainstream music scene. But for PVRIS, the success didn't happen overnight. Join us for our third installment of HOME STRETCH, as we head back to Lowell, Massachusetts to learn more about the band's come up. Episode 1 airs tomorrow at 9:30am PT / 12:30pm ET: Episode 2 out Thursday, 4/10 at 9:30am PT / 12:30pm ET Episode 3 out Thursday, 4/17 at 9:30am PT / 12:30pm PT #PVRIS

Live Nation

18,519 просмотров • 1 год назад

AMD invited us to their biggest launch of the year to check out the behind-the-scenes. At Advancing AI 2026, Anush Elangovan (VP, AI Software at AMD) and Quentin Anthony (VP, AI Engineering at Zyphra) join sophia dew on the floor to break down Helios, ROCm AI, and the Anthropic partnership, live the day it all shipped: 1:05 What actually changed today: AMD goes rack-scale 3:03 ROCm skills land in Claude Code and Codex 3:51 Why Zyphra bet its whole stack on AMD 3:57 Sparse mixture-of-experts and the HBM advantage 6:25 Why an open stack beats a closed one 8:52 The wall of logos: Anthropic, OpenAI, Meta 9:27 Why the frontier labs are committing gigawatts 11:30 Why the next wave will exceed expectations 17:02 Frontier models running on your own laptop + DeepSeek V4 Pro at 8-10 tokens/sec 17:41 How ROCm AI hits 3.3x inference gains 19:38 A kernel rewritten overnight with no humans in the loop 21:40 Do you still need kernel experts? 23:47 Why CPUs are critical for the agentic AI era 25:40 An agent that's been rewriting a driver for four months 28:14 The internet parallel, and the dark-fiber lesson
29:48

AMD invited us to their biggest launch of the year to check out the behind-the-scenes. At Advancing AI 2026, Anush Elangovan (VP, AI Software at AMD) and Quentin Anthony (VP, AI Engineering at Zyphra) join sophia dew on the floor to break down Helios, ROCm AI, and the Anthropic partnership, live the day it all shipped: 1:05 What actually changed today: AMD goes rack-scale 3:03 ROCm skills land in Claude Code and Codex 3:51 Why Zyphra bet its whole stack on AMD 3:57 Sparse mixture-of-experts and the HBM advantage 6:25 Why an open stack beats a closed one 8:52 The wall of logos: Anthropic, OpenAI, Meta 9:27 Why the frontier labs are committing gigawatts 11:30 Why the next wave will exceed expectations 17:02 Frontier models running on your own laptop + DeepSeek V4 Pro at 8-10 tokens/sec 17:41 How ROCm AI hits 3.3x inference gains 19:38 A kernel rewritten overnight with no humans in the loop 21:40 Do you still need kernel experts? 23:47 Why CPUs are critical for the agentic AI era 25:40 An agent that's been rewriting a driver for four months 28:14 The internet parallel, and the dark-fiber lesson

MTS

71,937 просмотров • 1 месяц назад

We're up 800%+ and we're only in the 3rd inning. There will be red days ahead, which means BUY. Here's the top 10 stocks to buy on every SINGLE dip until I tell you when to sell: 1. Micron $MU 2. Western Digital Corp. $WDC 3. Seagate Technology $STX 4. GE Vernova $GEV 5. Lam Research Corp. $LRCX 6. Sandisk $SNDK 7. Advanced Micro Devices $AMD 8. Corning $GLW 9. Kopin $KOPN 10. Arista Networks $ANET Never miss a bull-run again - all my buy and sell signals in Discord @
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We're up 800%+ and we're only in the 3rd inning. There will be red days ahead, which means BUY. Here's the top 10 stocks to buy on every SINGLE dip until I tell you when to sell: 1. Micron $MU 2. Western Digital Corp. $WDC 3. Seagate Technology $STX 4. GE Vernova $GEV 5. Lam Research Corp. $LRCX 6. Sandisk $SNDK 7. Advanced Micro Devices $AMD 8. Corning $GLW 9. Kopin $KOPN 10. Arista Networks $ANET Never miss a bull-run again - all my buy and sell signals in Discord @

StockWhale

295,592 просмотров • 3 месяцев назад

This is how you buy stocks that look like $MU, $SNDK, $RKLB, $AMD, $NBIS, $QCOM, $INTC, etc. right now. I teach you how to buy parabolic stocks the RIGHT way. The most important 3-minutes of your life that make and save millions of dollars: 1. Buy a very small position now. This gets rid of your FOMO and helps psychologically. 2. Buy when stocks reach the daily 62 / 79 EMA & 200 MA (needs to be in a clear uptrend). 3. I became a multi-millionaire because I buy LOW and trim HIGH. I followed these EXACT rules. 4. This is what I teach to my subscribers EVERYDAY. We buy LOW and trim HIGH. Follow my every move and you will retire in a few years.
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This is how you buy stocks that look like $MU, $SNDK, $RKLB, $AMD, $NBIS, $QCOM, $INTC, etc. right now. I teach you how to buy parabolic stocks the RIGHT way. The most important 3-minutes of your life that make and save millions of dollars: 1. Buy a very small position now. This gets rid of your FOMO and helps psychologically. 2. Buy when stocks reach the daily 62 / 79 EMA & 200 MA (needs to be in a clear uptrend). 3. I became a multi-millionaire because I buy LOW and trim HIGH. I followed these EXACT rules. 4. This is what I teach to my subscribers EVERYDAY. We buy LOW and trim HIGH. Follow my every move and you will retire in a few years.

AskLivermore

465,373 просмотров • 3 месяцев назад

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!
2:25

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!

Mike

104,109 просмотров • 3 месяцев назад

MY 5 BIGGEST TAKEAWAYS FROM $AMD ANALYST DAY 1. The pivot is official that AMD it’s building a platform with a goal is to sell full AI infrastructure: silicon, interconnect, software & racks that hyperscalers can deploy at scale. It’s chasing $NVDA vertical model but through an open ecosystem. 2. Data center is the company with Lisa guiding for ~35% CAGR, 57% gross margins & >$20 EPS by 2030. That requires DC biz to grow from ~$16B today to near $100B within five years which is a 6x run that only works if MI450 + Helios hit ramp targets & hyperscaler orders stack fast. 3. GPU is the upside but ROCm is the gatekeeper. The MI450 & yearly GPU cadence will define whether AMD becomes a real platform or stays a second source. Success hinges on ROCm feeling as stable and integrated as CUDA but partnerships with $META, $ORCL & OpenAI are the first real signs of ecosystem lift. 4. CPUs fund the fight with the server share near 40% gives AMD the profit engine it needs. As AI inference workloads push compute back toward CPUs, crossing 50% share does actually look realistic & that stable cash flow supports GPU & software expansion without breaking margins. 5. Michael Burry is wrong.
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MY 5 BIGGEST TAKEAWAYS FROM $AMD ANALYST DAY 1. The pivot is official that AMD it’s building a platform with a goal is to sell full AI infrastructure: silicon, interconnect, software & racks that hyperscalers can deploy at scale. It’s chasing $NVDA vertical model but through an open ecosystem. 2. Data center is the company with Lisa guiding for ~35% CAGR, 57% gross margins & >$20 EPS by 2030. That requires DC biz to grow from ~$16B today to near $100B within five years which is a 6x run that only works if MI450 + Helios hit ramp targets & hyperscaler orders stack fast. 3. GPU is the upside but ROCm is the gatekeeper. The MI450 & yearly GPU cadence will define whether AMD becomes a real platform or stays a second source. Success hinges on ROCm feeling as stable and integrated as CUDA but partnerships with $META, $ORCL & OpenAI are the first real signs of ecosystem lift. 4. CPUs fund the fight with the server share near 40% gives AMD the profit engine it needs. As AI inference workloads push compute back toward CPUs, crossing 50% share does actually look realistic & that stable cash flow supports GPU & software expansion without breaking margins. 5. Michael Burry is wrong.

Shay Boloor

244,819 просмотров • 9 месяцев назад

"Tomorrowland presents: Atmosphere" is back on Friday, November 14 at Antwerp Expo. Prepare for a night of high-energy performances from some of the most influential names in the scene. The full line-up drops on Thursday, September 4. Pre-Sale starts on Tuesday September 9. General Sale starts on Thursday September 11. Register now for exclusive access to the Pre-Sale:
0:25

"Tomorrowland presents: Atmosphere" is back on Friday, November 14 at Antwerp Expo. Prepare for a night of high-energy performances from some of the most influential names in the scene. The full line-up drops on Thursday, September 4. Pre-Sale starts on Tuesday September 9. General Sale starts on Thursday September 11. Register now for exclusive access to the Pre-Sale:

Tomorrowland

15,674 просмотров • 1 год назад

Crude is down more than 10% today. The Strait is open. Yesterday, Mark Newton CMT covered the technical setup behind crude and energy stocks, the S&P's 11-day recovery from a 100-day low to a 200-day high, and what the charts say about whether the rally holds into May. Keep watching for his full market roadmap, plus live analysis on $TSLA, $NVDA, $AMD, $DELL, $PLTR, $BABA, $MU, $AMAT, and more:
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Crude is down more than 10% today. The Strait is open. Yesterday, Mark Newton CMT covered the technical setup behind crude and energy stocks, the S&P's 11-day recovery from a 100-day low to a 200-day high, and what the charts say about whether the rally holds into May. Keep watching for his full market roadmap, plus live analysis on $TSLA, $NVDA, $AMD, $DELL, $PLTR, $BABA, $MU, $AMAT, and more:

Fundstrat Direct

15,616 просмотров • 4 месяцев назад

My interview with Greg Brockman (Greg Brockman), Cofounder and President of OpenAI. 0:00 Intro 1:06 Scaling Sora 2:18 Are transformer models the future? 4:29 Building with AMD 5:50 Other kinds of compute 7:28 Bottlenecks 9:17 Deciding where to invest 11:38 Decoupling of the internet 16:29 Why ChatGPT is different 17:59 Proactive AI 19:39 Compressing Intelligence 20:48 ChatGPT’s Longest Thought Ever 21:13 Sora 2 29:47 Is my job in danger? 31:51 Building on top of OpenAI 34:11 Humans in the loop 35:36 Fully generated software 39:25 Agentic Commerce Protocol 40:41 What does 2026 look like? 42:43 AGI
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My interview with Greg Brockman (Greg Brockman), Cofounder and President of OpenAI. 0:00 Intro 1:06 Scaling Sora 2:18 Are transformer models the future? 4:29 Building with AMD 5:50 Other kinds of compute 7:28 Bottlenecks 9:17 Deciding where to invest 11:38 Decoupling of the internet 16:29 Why ChatGPT is different 17:59 Proactive AI 19:39 Compressing Intelligence 20:48 ChatGPT’s Longest Thought Ever 21:13 Sora 2 29:47 Is my job in danger? 31:51 Building on top of OpenAI 34:11 Humans in the loop 35:36 Fully generated software 39:25 Agentic Commerce Protocol 40:41 What does 2026 look like? 42:43 AGI

Matthew Berman

45,086 просмотров • 10 месяцев назад

November 4, 1984 #49ers legend Joe Montana throws a career-high four interceptions, but guides his team to 16 unanswered second-half points in a 23-17 comeback win over the #Bengals at Candlestick. Joe Cool hits Freddie Solomon for the game-winning touchdown with 1:39 remaining to move the Niners' record to 9-1. #FTTB
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November 4, 1984 #49ers legend Joe Montana throws a career-high four interceptions, but guides his team to 16 unanswered second-half points in a 23-17 comeback win over the #Bengals at Candlestick. Joe Cool hits Freddie Solomon for the game-winning touchdown with 1:39 remaining to move the Niners' record to 9-1. #FTTB

Kevin Gallagher

13,709 просмотров • 9 месяцев назад